Huang Yan-Shu, Sheriff M Ziyan, Bachawala Sunidhi, Gonzalez Marcial, Nagy Zoltan K, Reklaitis Gintaras V
Davidson School of Chemical Engineering, Purdue University, West Lafayette, IN 47907, USA.
School of Mechanical Engineering, Purdue University, West Lafayette, IN 47907, USA.
Processes (Basel). 2021;9(9). doi: 10.3390/pr9091612. Epub 2021 Sep 8.
The transition from batch to continuous processes in the pharmaceutical industry has been driven by the potential improvement in process controllability, product quality homogeneity, and reduction of material inventory. A quality-by-control (QbC) approach has been implemented in a variety of pharmaceutical product manufacturing modalities to increase product quality through a three-level hierarchical control structure. In the implementation of the QbC approach it is common practice to simplify control algorithms by utilizing linearized models with constant model parameters. Nonlinear model predictive control (NMPC) can effectively deliver control functionality for highly sensitive variations and nonlinear multiple-input-multiple-output (MIMO) systems, which is essential for the highly regulated pharmaceutical manufacturing industry. This work focuses on developing and implementing NMPC in continuous manufacturing of solid dosage forms. To mitigate control degradation caused by plant-model mismatch, careful monitoring and continuous improvement strategies are studied. When moving horizon estimation (MHE) is integrated with NMPC, historical data in the past time window together with real-time data from the sensor network enable state estimation and accurate tracking of the highly sensitive model parameters. The adaptive model used in the NMPC strategy can compensate for process uncertainties, further reducing plant-model mismatch effects. The nonlinear mechanistic model used in both MHE and NMPC can predict the essential but complex powder properties and provide physical interpretation of abnormal events. The adaptive NMPC implementation and its real-time control performance analysis and practical applicability are demonstrated through a series of illustrative examples that highlight the effectiveness of the proposed approach for different scenarios of plant-model mismatch, while also incorporating glidant effects.
制药行业从间歇式生产向连续式生产的转变，是由过程可控性的潜在提升、产品质量均匀性的改善以及物料库存的减少所推动的。质量控制（QbC）方法已在多种药品生产模式中实施，通过三级分层控制结构提高产品质量。在实施QbC方法时，常见的做法是利用具有恒定模型参数的线性化模型来简化控制算法。非线性模型预测控制（NMPC）能够有效地为高度敏感的变化和非线性多输入多输出（MIMO）系统提供控制功能，这对于高度规范的制药制造业至关重要。这项工作专注于在固体剂型的连续制造中开发和实施NMPC。为了减轻因工厂模型不匹配导致的控制性能下降，研究了仔细的监测和持续改进策略。当将移动时域估计（MHE）与NMPC集成时，过去时间窗口内的历史数据以及来自传感器网络的实时数据能够实现状态估计，并精确跟踪高度敏感的模型参数。NMPC策略中使用的自适应模型可以补偿过程不确定性，进一步减少工厂模型不匹配的影响。MHE和NMPC中使用的非线性机理模型能够预测关键但复杂的粉末特性，并对异常事件提供物理解释。通过一系列示例展示了自适应NMPC的实施及其实时控制性能分析和实际适用性，这些示例突出了所提出方法在不同工厂模型不匹配场景下的有效性，同时还纳入了助流剂的影响。
